Businesses around the world largely consume a large amount of data and companies typically have a multitude of systems that are utilized by their employees to retrieve, view, analyze, manipulate, and/or enter data. It is not uncommon for a single user to utilize anywhere between half a dozen and a dozen separate applications each day for these purposes. Moreover, in many cases, desired information is only available through a distributed network of different types of data sources, fees, or repositories. These are often located on remote sources accessible through the internet, each having its own particular protocol for data access and retrieval. For companies involved in the financial industry, an employee may need to retrieve information from and multiple disparate market data sources, various online trading platforms and exchanges, news feeds, and even weather, traffic data, and various historical records. As a result, users must manually hunt for data or access and/or input data on multiple systems in various formats. This reduces efficiency and can introduce other issues as well.
The need to utilize a multitude of systems for unique and at times similar data also has problems because the same data item may be referred to differently on different platforms. As a result, each user must know the appropriate key words for the platform. This is a particularly acute issue in the financial services industry where users can be forced to manually work with and remember multiple different codes to reference the same instrument on different systems. For example, on one platform a commodity may be referred to as “West Texas Intermediate” while on others the same commodity is WTI, TI, or even CL. Similar issues exist for more mundane data fields, such as the date format (e.g., Mar-17, Mar17, March 2017, March-17, H17, 2017-03, Red March, Blue March, etc.) The user workflow fragmentation resulting from multiple applications, interfaces and syntaxes creates organizational inefficiencies increasing workloads and distracting employees from their core responsibilities. Similar differences may exist between companies. For example, two different companies (or even different departments or employees) may internally refer to the same commodity using different terminology. Combined, these disparities introduce many inefficiencies and opportunities for error.
The issues above are further magnified when user input to a data query system is provided by audio input and is processed using a speech recognition system. While audio input systems allow for rapid user input without a keyboard, speech recognition systems are prone to transcriptions errors, particularly where the speaker may have an accent that the system is not trained specifically to address.
Accordingly, there is a need for an improved method and system for processing user input that will allow users at various companies to access a variety of data sources through a conversational interfaces and without the need for the user to specifically select the source from which the requested data will be retrieved.
There is a further need for a query based data access system that can efficiently process user input queries to identify the particular data source corresponding to the query, forward the query in the proper format to the source of the data, and then retrieve and present the results in an appropriate format.
While an organization may have access to a large number of data sources, it may restrict access to these based on a user's authority level. There is a further need for such a system to efficiently process user input queries in a manner that accounts for restrictions not only in which data sources a user may query but on which queries can be made for that source.